blob: d2d602f433200e2f2974e9a85c1ca5c8fac7f904 [file] [log] [blame]
include: "../views/*.view.lkml"
explore: revenue_pdt_explore {
view_name: revenue_pdt
label: "revenue_pdt_explore label bis"
}
# view: revenue_pdt_explore {
# # Or, you could make this view a derived table, like this:
# derived_table: {
# sql: SELECT
# user_id as user_id
# , COUNT(*) as lifetime_orders
# , MAX(orders.created_at) as most_recent_purchase_at
# FROM orders
# GROUP BY user_id
# ;;
# }
#
# # Define your dimensions and measures here, like this:
# dimension: user_id {
# description: "Unique ID for each user that has ordered"
# type: number
# sql: ${TABLE}.user_id ;;
# }
#
# dimension: lifetime_orders {
# description: "The total number of orders for each user"
# type: number
# sql: ${TABLE}.lifetime_orders ;;
# }
#
# dimension_group: most_recent_purchase {
# description: "The date when each user last ordered"
# type: time
# timeframes: [date, week, month, year]
# sql: ${TABLE}.most_recent_purchase_at ;;
# }
#
# measure: total_lifetime_orders {
# description: "Use this for counting lifetime orders across many users"
# type: sum
# sql: ${lifetime_orders} ;;
# }
# }